{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "class Model_vec:\n",
    "    \n",
    "    def __init__(self, bow_shape, batch_size, dimension_size, learning_rate, vocabulary_size, boundary):\n",
    "        self.X = tf.placeholder(tf.float32, shape=[batch_size, bow_shape])\n",
    "        self.Y = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "        embeddings = tf.Variable(tf.random_uniform([bow_shape, dimension_size], boundary[0], boundary[1]))\n",
    "        embeddings = tf.matmul(self.X, embeddings)\n",
    "        nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, dimension_size], stddev = 1.0 / np.sqrt(dimension_size)))\n",
    "        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "        self.loss = tf.reduce_mean(tf.nn.nce_loss(weights = nce_weights, biases = nce_biases, labels = self.Y,\n",
    "                                                  inputs = embeddings, num_sampled = batch_size, num_classes = vocabulary_size))\n",
    "\n",
    "        self.optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(self.loss)\n",
    "        norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims = True))\n",
    "        self.normalized_embeddings = embeddings / norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "boundary = [-1, 1]\n",
    "batch_size = 20\n",
    "dimension_size = 300\n",
    "epoch = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn.datasets\n",
    "import re\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clear string\n",
    "def clearstring(string):\n",
    "    string = re.sub('[^A-Za-z ]+', '', string)\n",
    "    string = string.split(' ')\n",
    "    string = filter(None, string)\n",
    "    string = [y.strip() for y in string]\n",
    "    string = ' '.join(string)\n",
    "    return string.lower()\n",
    "\n",
    "# because of sklean.datasets read a document as a single element\n",
    "# so we want to split based on new line\n",
    "def separate_dataset(trainset):\n",
    "    datastring = []\n",
    "    datatarget = []\n",
    "    for i in range(len(trainset.data)):\n",
    "        data_ = trainset.data[i].split('\\n')\n",
    "        # python3, if python2, just remove list()\n",
    "        data_ = list(filter(None, data_))\n",
    "        for n in range(len(data_)):\n",
    "            data_[n] = clearstring(data_[n])\n",
    "        datastring += data_\n",
    "        for n in range(len(data_)):\n",
    "            datatarget.append(trainset.target[i])\n",
    "    return datastring, datatarget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\n",
      "416809\n",
      "416809\n"
     ]
    }
   ],
   "source": [
    "# you can change any encoding type\n",
    "trainset = sklearn.datasets.load_files(container_path = 'data', encoding = 'UTF-8')\n",
    "trainset.data, trainset.target = separate_dataset(trainset)\n",
    "print (trainset.target_names)\n",
    "print (len(trainset.data))\n",
    "print (len(trainset.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "combined = list(zip(trainset.data, trainset.target))\n",
    "random.shuffle(combined)\n",
    "\n",
    "trainset.data[:], trainset.target[:] = zip(*combined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bow = CountVectorizer(min_df=10).fit(trainset.data)\n",
    "out = bow.transform(trainset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_Y = np.array(trainset.target).reshape((-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:  0 avg loss:  0.00331325970318\n",
      "epoch:  1 avg loss:  0.00242805627395\n",
      "epoch:  2 avg loss:  0.00134093601278\n",
      "epoch:  3 avg loss:  0.00176551053712\n",
      "epoch:  4 avg loss:  0.0016130959919\n",
      "epoch:  5 avg loss:  0.00206702043823\n",
      "epoch:  6 avg loss:  0.000990094150097\n",
      "epoch:  7 avg loss:  0.000851506143522\n",
      "epoch:  8 avg loss:  0.000532992375789\n",
      "epoch:  9 avg loss:  0.00111142980389\n",
      "epoch:  10 avg loss:  0.00103610737996\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-57b125af88f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtotal_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         loss, _ = sess.run([model.loss, model.optimizer], feed_dict = {model.X: out[k: k + batch_size, :].todense(),\n\u001b[0m\u001b[1;32m      8\u001b[0m                                                                        model.Y: label_Y[k: k + batch_size, :]})\n\u001b[1;32m      9\u001b[0m     \u001b[0mtotal_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/scipy/sparse/base.py\u001b[0m in \u001b[0;36mtodense\u001b[0;34m(self, order, out)\u001b[0m\n\u001b[1;32m    719\u001b[0m             \u001b[0;31m`\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0mobject\u001b[0m \u001b[0mthat\u001b[0m \u001b[0mshares\u001b[0m \u001b[0mthe\u001b[0m \u001b[0msame\u001b[0m \u001b[0mmemory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    720\u001b[0m         \"\"\"\n\u001b[0;32m--> 721\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    723\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/scipy/sparse/compressed.py\u001b[0m in \u001b[0;36mtoarray\u001b[0;34m(self, order, out)\u001b[0m\n\u001b[1;32m    962\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    963\u001b[0m         \u001b[0;34m\"\"\"See the docstring for `spmatrix.toarray`.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtocoo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    965\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    966\u001b[0m     \u001b[0;31m##############################################################\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/scipy/sparse/compressed.py\u001b[0m in \u001b[0;36mtocoo\u001b[0;34m(self, copy)\u001b[0m\n\u001b[1;32m    956\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcoo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcoo_matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m         return coo_matrix((self.data, (row, col)), self.shape, copy=copy,\n\u001b[0;32m--> 958\u001b[0;31m                           dtype=self.dtype)\n\u001b[0m\u001b[1;32m    959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m     \u001b[0mtocoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtocoo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/scipy/sparse/coo.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, arg1, shape, dtype, copy)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0m_data_matrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m                 \u001b[0mM\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "model = Model_vec(out.shape[1], batch_size, dimension_size, learning_rate, out.shape[0], boundary)\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for i in range(epoch):\n",
    "    total_loss = 0\n",
    "    for k in range(0, (out.shape[0] // batch_size) * batch_size, batch_size):\n",
    "        loss, _ = sess.run([model.loss, model.optimizer], feed_dict = {model.X: out[k: k + batch_size, :].todense(),\n",
    "                                                                       model.Y: label_Y[k: k + batch_size, :]})\n",
    "    total_loss += loss\n",
    "    print('epoch: ', i, 'avg loss: ', total_loss / (out.shape[0] // batch_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorized = np.zeros(((out.shape[0] // batch_size) * batch_size, dimension_size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in range(0, (out.shape[0] // batch_size) * batch_size, batch_size):\n",
    "    vectorized[k: k + batch_size, :] = sess.run(model.normalized_embeddings, feed_dict = {model.X: out[k: k + batch_size, :].todense()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cross_validation import train_test_split\n",
    "_, vect_temp, _, Y_temp = train_test_split(vectorized, trainset.target[:vectorized.shape[0]], test_size = 0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_2d = TSNE(n_components = 2).fit_transform(vect_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vw/z5CwAAP/vZjzB//gIsXPglPP30r1Ff/wkkKYorr1yOq69ertr1qEGVAGvNmjXYtGkT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SWo3hwUpk+Gm9EcLdVNW1svFMzJFYCuVx4PtN8gVM/5zPt7LSdpe0Us26E6jWxgPdCQxECn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\nIiIiItIYEywiIiIijf0/YRv62hzmZ5oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f080c02c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "plt.figure(figsize=(10, 10))\n",
    "colors = sns.color_palette(n_colors = len(trainset.target_names))\n",
    "y_train_reshape = np.array(Y_temp)\n",
    "for no, _ in enumerate(np.unique(y_train_reshape)):\n",
    "    plt.scatter(embed_2d[y_train_reshape == no, 0], embed_2d[y_train_reshape == no, 1], c = colors[no], label = trainset.target_names[no])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('vector.p', 'wb') as fopen:\n",
    "    pickle.dump(vectorized, fopen)\n",
    "with open('label-y.p', 'wb') as fopen:\n",
    "    pickle.dump(trainset.target[:vectorized.shape[0]], fopen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_X, test_X, train_Y, test_Y = train_test_split(vectorized, trainset.target[:vectorized.shape[0]], test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/lightgbm/basic.py:642: UserWarning: max_bin keyword has been found in `params` and will be ignored. Please use max_bin argument of the Dataset constructor to pass this parameter.\n",
      "  'Please use {0} argument of the Dataset constructor to pass this parameter.'.format(key))\n",
      "/usr/local/lib/python3.5/dist-packages/lightgbm/basic.py:648: LGBMDeprecationWarning: The `max_bin` parameter is deprecated and will be removed in 2.0.12 version. Please use `params` to pass this parameter.\n",
      "  'Please use `params` to pass this parameter.', LGBMDeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's multi_logloss: 1.76132\tvalid_1's multi_logloss: 1.76207\n",
      "Training until validation scores don't improve for 20 rounds.\n",
      "[2]\tvalid_0's multi_logloss: 1.73429\tvalid_1's multi_logloss: 1.73585\n",
      "[3]\tvalid_0's multi_logloss: 1.71066\tvalid_1's multi_logloss: 1.71288\n",
      "[4]\tvalid_0's multi_logloss: 1.6893\tvalid_1's multi_logloss: 1.69238\n",
      "[5]\tvalid_0's multi_logloss: 1.67051\tvalid_1's multi_logloss: 1.67433\n",
      "[6]\tvalid_0's multi_logloss: 1.65326\tvalid_1's multi_logloss: 1.65775\n",
      "[7]\tvalid_0's multi_logloss: 1.63799\tvalid_1's multi_logloss: 1.64308\n",
      "[8]\tvalid_0's multi_logloss: 1.64465\tvalid_1's multi_logloss: 1.64946\n",
      "[9]\tvalid_0's multi_logloss: 1.62993\tvalid_1's multi_logloss: 1.63536\n",
      "[10]\tvalid_0's multi_logloss: 1.61651\tvalid_1's multi_logloss: 1.62253\n",
      "[11]\tvalid_0's multi_logloss: 1.60429\tvalid_1's multi_logloss: 1.61085\n",
      "[12]\tvalid_0's multi_logloss: 1.609\tvalid_1's multi_logloss: 1.61533\n",
      "[13]\tvalid_0's multi_logloss: 1.59745\tvalid_1's multi_logloss: 1.6044\n",
      "[14]\tvalid_0's multi_logloss: 1.58682\tvalid_1's multi_logloss: 1.5944\n",
      "[15]\tvalid_0's multi_logloss: 1.57706\tvalid_1's multi_logloss: 1.58521\n",
      "[16]\tvalid_0's multi_logloss: 1.56804\tvalid_1's multi_logloss: 1.57688\n",
      "[17]\tvalid_0's multi_logloss: 1.55982\tvalid_1's multi_logloss: 1.56935\n",
      "[18]\tvalid_0's multi_logloss: 1.55203\tvalid_1's multi_logloss: 1.56212\n",
      "[19]\tvalid_0's multi_logloss: 1.54478\tvalid_1's multi_logloss: 1.55536\n",
      "[20]\tvalid_0's multi_logloss: 1.53805\tvalid_1's multi_logloss: 1.54926\n",
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      "[24]\tvalid_0's multi_logloss: 1.52151\tvalid_1's multi_logloss: 1.53413\n",
      "[25]\tvalid_0's multi_logloss: 1.51599\tvalid_1's multi_logloss: 1.52917\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[116]\tvalid_0's multi_logloss: 1.43052\tvalid_1's multi_logloss: 1.45596\n",
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      "[191]\tvalid_0's multi_logloss: 1.38183\tvalid_1's multi_logloss: 1.41617\n",
      "[192]\tvalid_0's multi_logloss: 1.37959\tvalid_1's multi_logloss: 1.41435\n",
      "[193]\tvalid_0's multi_logloss: 1.3774\tvalid_1's multi_logloss: 1.41261\n",
      "[194]\tvalid_0's multi_logloss: 1.37863\tvalid_1's multi_logloss: 1.41362\n",
      "[195]\tvalid_0's multi_logloss: 1.37977\tvalid_1's multi_logloss: 1.41454\n",
      "[196]\tvalid_0's multi_logloss: 1.3775\tvalid_1's multi_logloss: 1.41266\n",
      "[197]\tvalid_0's multi_logloss: 1.37851\tvalid_1's multi_logloss: 1.41346\n",
      "[198]\tvalid_0's multi_logloss: 1.37975\tvalid_1's multi_logloss: 1.41447\n",
      "[199]\tvalid_0's multi_logloss: 1.37752\tvalid_1's multi_logloss: 1.4127\n",
      "[200]\tvalid_0's multi_logloss: 1.37892\tvalid_1's multi_logloss: 1.41384\n",
      "[201]\tvalid_0's multi_logloss: 1.38025\tvalid_1's multi_logloss: 1.41493\n",
      "[202]\tvalid_0's multi_logloss: 1.38154\tvalid_1's multi_logloss: 1.41598\n",
      "[203]\tvalid_0's multi_logloss: 1.37924\tvalid_1's multi_logloss: 1.41413\n",
      "[204]\tvalid_0's multi_logloss: 1.38035\tvalid_1's multi_logloss: 1.41504\n",
      "[205]\tvalid_0's multi_logloss: 1.38162\tvalid_1's multi_logloss: 1.41607\n",
      "[206]\tvalid_0's multi_logloss: 1.37933\tvalid_1's multi_logloss: 1.4142\n",
      "[207]\tvalid_0's multi_logloss: 1.38057\tvalid_1's multi_logloss: 1.41522\n",
      "[208]\tvalid_0's multi_logloss: 1.37823\tvalid_1's multi_logloss: 1.41329\n",
      "[209]\tvalid_0's multi_logloss: 1.37961\tvalid_1's multi_logloss: 1.41441\n",
      "[210]\tvalid_0's multi_logloss: 1.38079\tvalid_1's multi_logloss: 1.41538\n",
      "[211]\tvalid_0's multi_logloss: 1.382\tvalid_1's multi_logloss: 1.41637\n",
      "[212]\tvalid_0's multi_logloss: 1.37962\tvalid_1's multi_logloss: 1.41451\n",
      "[213]\tvalid_0's multi_logloss: 1.37735\tvalid_1's multi_logloss: 1.41274\n",
      "Early stopping, best iteration is:\n",
      "[193]\tvalid_0's multi_logloss: 1.3774\tvalid_1's multi_logloss: 1.41261\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='dart', colsample_bytree=0.4, device='gpu',\n",
       "        learning_rate=0.1, max_bin=255, max_depth=-1, min_child_samples=20,\n",
       "        min_child_weight=0.001, min_split_gain=0.0, n_estimators=10000,\n",
       "        n_jobs=-1, num_leaves=31, objective='multiclass',\n",
       "        random_state=None, reg_alpha=0.0, reg_lambda=0.0005, silent=False,\n",
       "        subsample=0.8, subsample_for_bin=200000, subsample_freq=1)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import lightgbm as lgb\n",
    "params_lgd = {\n",
    "    'boosting_type': 'dart',\n",
    "    'objective': 'multiclass',\n",
    "    'colsample_bytree': 0.4,\n",
    "    'subsample': 0.8,\n",
    "    'learning_rate': 0.1,\n",
    "    'silent': False,\n",
    "    'n_estimators': 10000,\n",
    "    'reg_lambda': 0.0005,\n",
    "    'device':'gpu'\n",
    "    }\n",
    "clf = lgb.LGBMClassifier(**params_lgd)\n",
    "clf.fit(train_X,train_Y, eval_set=[(train_X,train_Y), (test_X,test_Y)], \n",
    "        eval_metric='logloss', early_stopping_rounds=20, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy validation set:  0.45790547025\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "      anger       0.73      0.06      0.12     11574\n",
      "       fear       0.76      0.05      0.10      9464\n",
      "        joy       0.44      0.83      0.57     27958\n",
      "       love       0.86      0.01      0.01      7033\n",
      "    sadness       0.48      0.56      0.51     24322\n",
      "   surprise       0.80      0.02      0.04      3009\n",
      "\n",
      "avg / total       0.58      0.46      0.37     83360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "predicted = clf.predict(test_X)\n",
    "print('accuracy validation set: ', np.mean(predicted == test_Y))\n",
    "\n",
    "# print scores\n",
    "print(metrics.classification_report(test_Y, predicted, target_names = trainset.target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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